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Comparison of GMM performances Specifically how GMMs adapted from UBMs performe compared to unadapted GMMs





In [2]:



    


# general imports
import os
import pickle
import numpy as np
import copy



    











In [3]:



    


# import Scykit learn GMM library
from sklearn import mixture



    











In [4]:



    


# import custom functions
import sys
# path to libraries
# currently in ../scripts-lib/
tool_path = os.path.abspath('../scripts-lib')

if tool_path not in sys.path:
    sys.path.append(tool_path)
import lib_phones as lph

# print the loaded functions
print dir(lph)[5:]



    


















    






['find_phone_index', 'load_phone_file']

















In [5]:



    


# load phone list
phone_path = os.path.abspath('../datasets/TIMIT-MFCCs/TIMIT_phone_list.txt')
phone_list = lph.load_phone_file(phone_path)
print len(phone_list), phone_list



    


















    






61 ['aa', 'ae', 'ah', 'ao', 'aw', 'ax', 'ax-h', 'axr', 'ay', 'b', 'bcl', 'ch', 'd', 'dcl', 'dh', 'dx', 'eh', 'el', 'em', 'en', 'eng', 'epi', 'er', 'ey', 'f', 'g', 'gcl', 'h#', 'hh', 'hv', 'ih', 'ix', 'iy', 'jh', 'k', 'kcl', 'l', 'm', 'n', 'ng', 'nx', 'ow', 'oy', 'p', 'pau', 'pcl', 'q', 'r', 's', 'sh', 't', 'tcl', 'th', 'uh', 'uw', 'ux', 'v', 'w', 'y', 'z', 'zh']

















In [6]:



    


# load adapted GMMs pickled file

pickle_dir = os.path.abspath('../datasets/TIMIT Pickled Data')

pickle_name = 'TIMIT_gmm_adapted_dict.pckl'

agmm_dict = pickle.load( open(pickle_dir + os.sep + pickle_name, "rb") )
print "loaded adapted gmm from ", pickle_dir + os.sep + pickle_name



    


















    






loaded adapted gmm from  C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT Pickled Data\TIMIT_gmm_adapted_dict.pckl

















In [7]:



    


# load un-adapted GMMs pickled file

pickle_name = 'TIMIT_gmm_unadapted_dict.pckl'

ugmm_dict = pickle.load( open(pickle_dir + os.sep + pickle_name, "rb") )
print "loaded unadapted gmm from ", pickle_dir + os.sep + pickle_name



    


















    






loaded unadapted gmm from  C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT Pickled Data\TIMIT_gmm_unadapted_dict.pckl

















In [8]:



    


#load test mfccs into sklearn observations, each frame is an obs

test_TIMIT_dir = os.path.abspath('../datasets/TIMIT-MFCCs/test')

#create individual obs list for each phone type
test_phone_obs_dict = {}
for phone in phone_list:
    test_phone_obs_dict[phone] = []

# complete obs 
test_obs = []
test_obs_labels = []

# walk the directories
for (path, dirs, files) in os.walk(test_TIMIT_dir):
    print "working in path : " + path

    for file in files:
        # skip the SA files
        #dev, only work on file si1573.mfcc.csv     "si1573" in file and
        if ".mfcc" in file  and "sa" not in file:
            #check if corresponding .phn file exists
            if not os.path.exists(path + "/" + file[:-8] + "phn"):
                print path + "/" + file[:-8] + "phn"
                print "corresponding .phn file does not exist!"
            else:
                
                print "working on: " + file
#                 print "from path : " + path

                # open the files
                mfcc_file = open(path + "/" + file)
                phn_file = open(path + "/" + file[:-8] + "phn")

                # extract phone times
                phone_times = []
                for phn_line in phn_file:
                    phone_times.append(phn_line.split())
                # transpose for easier use
                phone_times = map(list, zip(*phone_times))

                # skip mfcc_file header
                next(mfcc_file)

                # reset frame count
                frame_cnt = 0

                # for each line of mfcc_file
                for mfcc_line in mfcc_file:

                    # increment frame count
                    frame_cnt += 1 

                    # print "frame line #:", frame_cnt 

                    # frame start time in seconds
                    start_t = mfcc_line.split(";")[1]

                    # create frame (skiping first 2 values, frame_index and frame_time)
                    frame = map( float,  mfcc_line.split(";")[2:])
                    # print numpy.shape(frame)
                    # print frame

                    # find correspond phoneme and index in the list
                    phn_index = lph.find_phone_index(start_t, phone_times, phone_list)

                    # add to instances for corresponding phone
                    test_phone_obs_dict[phone_list[phn_index]].append(frame)
                    # add to instances
                    test_obs.append(frame)
                    test_obs_labels.append(phone_list[phn_index])



    


















    






working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr1
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr1\faks0
working on: si1573.mfcc.csv
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working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr7\mrpc0
working on: si1753.mfcc.csv
working on: si493.mfcc.csv
working on: si933.mfcc.csv
working on: sx133.mfcc.csv
working on: sx223.mfcc.csv
working on: sx313.mfcc.csv
working on: sx403.mfcc.csv
working on: sx43.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr7\mtwh0
working on: si1190.mfcc.csv
working on: si1629.mfcc.csv
working on: si1820.mfcc.csv
working on: sx110.mfcc.csv
working on: sx20.mfcc.csv
working on: sx200.mfcc.csv
working on: sx290.mfcc.csv
working on: sx380.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\fcmh1
working on: si1493.mfcc.csv
working on: si2123.mfcc.csv
working on: si863.mfcc.csv
working on: sx143.mfcc.csv
working on: sx233.mfcc.csv
working on: sx323.mfcc.csv
working on: sx413.mfcc.csv
working on: sx53.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\fjsj0
working on: si1484.mfcc.csv
working on: si2114.mfcc.csv
working on: si854.mfcc.csv
working on: sx134.mfcc.csv
working on: sx224.mfcc.csv
working on: sx314.mfcc.csv
working on: sx404.mfcc.csv
working on: sx44.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\fmld0
working on: si2185.mfcc.csv
working on: si822.mfcc.csv
working on: si925.mfcc.csv
working on: sx115.mfcc.csv
working on: sx205.mfcc.csv
working on: sx25.mfcc.csv
working on: sx295.mfcc.csv
working on: sx385.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\majc0
working on: si1946.mfcc.csv
working on: si2095.mfcc.csv
working on: si835.mfcc.csv
working on: sx115.mfcc.csv
working on: sx205.mfcc.csv
working on: sx25.mfcc.csv
working on: sx295.mfcc.csv
working on: sx385.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mdaw1
working on: si1453.mfcc.csv
working on: si2083.mfcc.csv
working on: si823.mfcc.csv
working on: sx103.mfcc.csv
working on: sx13.mfcc.csv
working on: sx193.mfcc.csv
working on: sx283.mfcc.csv
working on: sx373.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mjln0
working on: si1449.mfcc.csv
working on: si2079.mfcc.csv
working on: si819.mfcc.csv
working on: sx189.mfcc.csv
working on: sx279.mfcc.csv
working on: sx369.mfcc.csv
working on: sx9.mfcc.csv
working on: sx99.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mjtc0
working on: si1460.mfcc.csv
working on: si2090.mfcc.csv
working on: si830.mfcc.csv
working on: sx110.mfcc.csv
working on: sx20.mfcc.csv
working on: sx200.mfcc.csv
working on: sx290.mfcc.csv
working on: sx380.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mjth0
working on: si1296.mfcc.csv
working on: si1926.mfcc.csv
working on: si666.mfcc.csv
working on: sx126.mfcc.csv
working on: sx216.mfcc.csv
working on: sx306.mfcc.csv
working on: sx36.mfcc.csv
working on: sx396.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mpam0
working on: si1189.mfcc.csv
working on: si1819.mfcc.csv
working on: si1961.mfcc.csv
working on: sx109.mfcc.csv
working on: sx19.mfcc.csv
working on: sx199.mfcc.csv
working on: sx289.mfcc.csv
working on: sx379.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mres0
working on: si1217.mfcc.csv
working on: si1847.mfcc.csv
working on: si587.mfcc.csv
working on: sx137.mfcc.csv
working on: sx227.mfcc.csv
working on: sx317.mfcc.csv
working on: sx407.mfcc.csv
working on: sx47.mfcc.csv
working in path : C:\Users\FG\Desktop\PhD\Research\Reservoirs\datasets\TIMIT-MFCCs\test\dr8\mslb0
working on: si1193.mfcc.csv
working on: si1823.mfcc.csv
working on: si563.mfcc.csv
working on: sx113.mfcc.csv
working on: sx203.mfcc.csv
working on: sx23.mfcc.csv
working on: sx293.mfcc.csv
working on: sx383.mfcc.csv

















In [9]:



    


# average number of observations for each phone
np.mean([len(phn_list) for phn_list in test_phone_obs_dict.values()])



    


















    
Out[9]:








6736.3934426229507

















In [10]:



    


# compute log probs
for phone in phone_list:
    if phone in agmm_dict and phone in ugmm_dict:
        print 'Phone:', phone
        print 'adapted gmm average log prob:', np.mean(agmm_dict[phone].score(test_phone_obs_dict[phone]))
        print 'unadapted gmm average log prob:', np.mean(ugmm_dict[phone].score(test_phone_obs_dict[phone]))

    else:
        print 'no model for', phone



    


















    






Phone: aa
adapted gmm average log prob: -94.6676338505
unadapted gmm average log prob: -94.6951896424
Phone: ae
adapted gmm average log prob: -93.7180105925
unadapted gmm average log prob: -93.7776945385
Phone: ah
adapted gmm average log prob: -99.6445498181
unadapted gmm average log prob: -99.6578306456
Phone: ao
adapted gmm average log prob: -94.2672854013
unadapted gmm average log prob: -94.2499251818
Phone: aw
adapted gmm average log prob: -95.6111993063
unadapted gmm average log prob: -95.6292778689
Phone: ax
adapted gmm average log prob: -103.612528751
unadapted gmm average log prob: -103.589686954
Phone: ax-h
adapted gmm average log prob: -140.503375764
unadapted gmm average log prob: -140.04682134
Phone: axr
adapted gmm average log prob: -99.8871014083
unadapted gmm average log prob: -99.8717012764
Phone: ay
adapted gmm average log prob: -92.5477107148
unadapted gmm average log prob: -92.5404881158
Phone: b
adapted gmm average log prob: -109.246722307
unadapted gmm average log prob: -109.172143871
Phone: bcl
adapted gmm average log prob: -99.5732988745
unadapted gmm average log prob: -99.5196576946
Phone: ch
adapted gmm average log prob: -98.1577022619
unadapted gmm average log prob: -98.1194634839
Phone: d
adapted gmm average log prob: -105.542184742
unadapted gmm average log prob: -105.522234604
Phone: dcl
adapted gmm average log prob: -102.8241549
unadapted gmm average log prob: -102.811765916
Phone: dh
adapted gmm average log prob: -103.652669826
unadapted gmm average log prob: -103.495610558
Phone: dx
adapted gmm average log prob: -105.187731953
unadapted gmm average log prob: -105.440116737
Phone: eh
adapted gmm average log prob: -97.5560969159
unadapted gmm average log prob: -97.5325666402
Phone: el
adapted gmm average log prob: -101.246407995
unadapted gmm average log prob: -101.465603834
Phone: em
adapted gmm average log prob: -298.035458494
unadapted gmm average log prob: -281.140452129
Phone: en
adapted gmm average log prob: -108.133332799
unadapted gmm average log prob: -107.95807289
no model for eng
Phone: epi
adapted gmm average log prob: -111.20468361
unadapted gmm average log prob: -111.169687087
Phone: er
adapted gmm average log prob: -96.273598498
unadapted gmm average log prob: -96.3214157543
Phone: ey
adapted gmm average log prob: -93.301945304
unadapted gmm average log prob: -93.2960224853
Phone: f
adapted gmm average log prob: -93.2228994378
unadapted gmm average log prob: -93.2490028066
Phone: g
adapted gmm average log prob: -114.230947937
unadapted gmm average log prob: -114.360710655
Phone: gcl
adapted gmm average log prob: -107.412266081
unadapted gmm average log prob: -107.332388357
Phone: h#
adapted gmm average log prob: -87.731614805
unadapted gmm average log prob: -87.7560868368
Phone: hh
adapted gmm average log prob: -103.408181588
unadapted gmm average log prob: -103.330274288
Phone: hv
adapted gmm average log prob: -104.357811765
unadapted gmm average log prob: -104.351059997
Phone: ih
adapted gmm average log prob: -99.0012659647
unadapted gmm average log prob: -99.0214219618
Phone: ix
adapted gmm average log prob: -102.370487848
unadapted gmm average log prob: -102.356424284
Phone: iy
adapted gmm average log prob: -95.5688270851
unadapted gmm average log prob: -95.5661425723
Phone: jh
adapted gmm average log prob: -99.9694568565
unadapted gmm average log prob: -99.8426955408
Phone: k
adapted gmm average log prob: -103.013340287
unadapted gmm average log prob: -103.017875859
Phone: kcl
adapted gmm average log prob: -102.843775998
unadapted gmm average log prob: -102.898494312
Phone: l
adapted gmm average log prob: -100.812762225
unadapted gmm average log prob: -100.789368988
Phone: m
adapted gmm average log prob: -100.504387049
unadapted gmm average log prob: -100.477441937
Phone: n
adapted gmm average log prob: -102.784540653
unadapted gmm average log prob: -102.798950322
Phone: ng
adapted gmm average log prob: -105.314540874
unadapted gmm average log prob: -105.285250124
Phone: nx
adapted gmm average log prob: -116.754491367
unadapted gmm average log prob: -116.443415252
Phone: ow
adapted gmm average log prob: -96.5594994888
unadapted gmm average log prob: -96.4424196534
Phone: oy
adapted gmm average log prob: -102.386495697
unadapted gmm average log prob: -102.139988657
Phone: p
adapted gmm average log prob: -103.014339664
unadapted gmm average log prob: -103.09367267
Phone: pau
adapted gmm average log prob: -96.0504327871
unadapted gmm average log prob: -96.0434730783
Phone: pcl
adapted gmm average log prob: -95.7127425619
unadapted gmm average log prob: -95.6764891426
Phone: q
adapted gmm average log prob: -105.587142159
unadapted gmm average log prob: -105.486289858
Phone: r
adapted gmm average log prob: -99.4689887031
unadapted gmm average log prob: -99.4769816798
Phone: s
adapted gmm average log prob: -93.5358488124
unadapted gmm average log prob: -93.6127023771
Phone: sh
adapted gmm average log prob: -92.2979239822
unadapted gmm average log prob: -92.2684222682
Phone: t
adapted gmm average log prob: -101.07425127
unadapted gmm average log prob: -101.152446462
Phone: tcl
adapted gmm average log prob: -100.758872611
unadapted gmm average log prob: -100.78170859
Phone: th
adapted gmm average log prob: -101.036098264
unadapted gmm average log prob: -101.069316381
Phone: uh
adapted gmm average log prob: -112.295011488
unadapted gmm average log prob: -112.25012375
Phone: uw
adapted gmm average log prob: -104.959178419
unadapted gmm average log prob: -104.762234582
Phone: ux
adapted gmm average log prob: -97.2767908202
unadapted gmm average log prob: -97.3107700735
Phone: v
adapted gmm average log prob: -99.4826350722
unadapted gmm average log prob: -99.4845758706
Phone: w
adapted gmm average log prob: -98.8454553318
unadapted gmm average log prob: -98.789530333
Phone: y
adapted gmm average log prob: -100.533048438
unadapted gmm average log prob: -100.960683015
Phone: z
adapted gmm average log prob: -97.4034226455
unadapted gmm average log prob: -97.3834223213
Phone: zh
adapted gmm average log prob: -144.536256424
unadapted gmm average log prob: -138.088549724

















In [11]:



    


# average difference
agmm_log_prob_avgs = [np.mean(agmm_dict[phone].score(test_phone_obs_dict[phone])) for phone in agmm_dict.keys()]
ugmm_log_prob_avgs = [np.mean(ugmm_dict[phone].score(test_phone_obs_dict[phone])) for phone in ugmm_dict.keys()]



    


















    






[-94.667633850476321, -101.24640799472776, -98.157702261933892, -144.53625642445795, -97.556096915857495, -102.37048784843427, -99.644549818145691, -96.559499488760849, -94.267285401270982, -99.001265964677827, -100.75887261063575, -108.1333327992691, -93.301945303955819, -95.611199306312969, -103.61252875106683, -92.5477107147601, -93.718010592537183, -96.2735984979591, -96.050432787057787, -107.41226608107513, -105.31454087438503, -116.75449136653056, -95.568827085070424, -99.468988703149648, -95.712742561909266, -101.07425127023319, -99.887101408280216, -102.8241549004379, -101.03609826423182, -103.65266982580231, -102.84377599762746, -111.20468361042199, -104.35781176497116, -100.50438704896845, -102.38649569652617, -103.40818158763052, -99.969456856520139, -105.18773195327002, -140.50337576367789, -298.03545849392759, -105.54218474237244, -99.573298874527865, -109.24672230712537, -97.276790820205036, -114.23094793687656, -93.222899437848199, -103.01334028716332, -87.731614804952784, -100.81276222462053, -102.78454065321613, -105.58714215890173, -103.01433966441299, -93.535848812395173, -92.297923982240135, -112.29501148757963, -98.845455331788457, -99.482635072186099, -100.53304843832667, -97.403422645510219, -104.9591784191025] [-94.695189642392819, -101.4656038341226, -98.119463483890542, -138.08854972379854, -97.532566640197032, -102.35642428353012, -99.657830645573895, -96.44241965342475, -94.249925181816451, -99.021421961785919, -100.78170858955896, -107.95807288954406, -93.296022485269688, -95.629277868922856, -103.58968695422833, -92.540488115821177, -93.777694538466775, -96.321415754276785, -96.043473078343112, -107.33238835707444, -105.28525012409965, -116.44341525179576, -95.566142572266159, -99.476981679832633, -95.676489142639852, -101.15244646168463, -99.87170127641096, -102.81176591567463, -101.06931638129578, -103.4956105575552, -102.89849431201964, -111.16968708679396, -104.35105999711882, -100.47744193682054, -102.13998865690303, -103.33027428763049, -99.842695540784035, -105.44011673668702, -140.04682133993705, -281.14045212932859, -105.52223460421976, -99.519657694642191, -109.17214387129198, -97.310770073523628, -114.36071065456338, -93.249002806578545, -103.01787585946646, -87.75608683682951, -100.78936898779322, -102.79895032172912, -105.48628985790097, -103.09367266952586, -93.612702377055527, -92.268422268198591, -112.25012374973618, -98.789530332963523, -99.484575870609291, -100.96068301480474, -97.383422321303343, -104.76223458162301]

















In [12]:



    


print [a>b for (a,b) in zip(agmm_log_prob_avgs, ugmm_log_prob_avgs)].count(True)
print [a>b for (a,b) in zip(agmm_log_prob_avgs, ugmm_log_prob_avgs)].count(False)



    


















    






23
37

















In [15]:



    


# checking

agmm_log_prob_avgs_2 = []
ugmm_log_prob_avgs_2 = []

improvements = 0
deteriorations = 0

for phone in phone_list:
    if phone in agmm_dict and phone in ugmm_dict:
        print 'Phone:', phone
        
        v1 = np.mean(agmm_dict[phone].score(test_phone_obs_dict[phone]))
        v2 = np.mean(ugmm_dict[phone].score(test_phone_obs_dict[phone]))
        
        agmm_log_prob_avgs_2.append(v1)
        ugmm_log_prob_avgs_2.append(v2)
        
        if v1 > v2:
            improvements += 1
        else:
            deteriorations +=1

    else:
        print 'no model for', phone



    


















    






Phone: aa
Phone: ae
Phone: ah
Phone: ao
Phone: aw
Phone: ax
Phone: ax-h
Phone: axr
Phone: ay
Phone: b
Phone: bcl
Phone: ch
Phone: d
Phone: dcl
Phone: dh
Phone: dx
Phone: eh
Phone: el
Phone: em
Phone: en
no model for eng
Phone: epi
Phone: er
Phone: ey
Phone: f
Phone: g
Phone: gcl
Phone: h#
Phone: hh
Phone: hv
Phone: ih
Phone: ix
Phone: iy
Phone: jh
Phone: k
Phone: kcl
Phone: l
Phone: m
Phone: n
Phone: ng
Phone: nx
Phone: ow
Phone: oy
Phone: p
Phone: pau
Phone: pcl
Phone: q
Phone: r
Phone: s
Phone: sh
Phone: t
Phone: tcl
Phone: th
Phone: uh
Phone: uw
Phone: ux
Phone: v
Phone: w
Phone: y
Phone: z
Phone: zh

















In [16]:



    


print improvements, deteriorations



    


















    






23 37

















In [ ]:

Content source: grezesf/Research

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